Variational Robust Subspace Clustering with Mean Update Algorithm

In this paper, we propose an efficient variational Bayesian (VB) solver for a robust variant of low-rank subspace clustering (LRSC). VB learning offers automatic model selection without parameter tuning. However, it is typically performed by local search with update rules derived from conditional conjugacy, and therefore prone to local minima problem. Instead, we use an approximate global solver for LRSC with an element-wise sparse term to make it robust against spiky noise. In experiment, our method (mean update solver for robust LRSC), outperforms the original LRSC, as well as the robust LRSC with the standard VB solver.